Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control
This paper studies the asymptotic stability of fractional-order neural networks (FONNs) with time delay utilizing a sampled-data controller. Firstly, a novel class of Lyapunov–Krasovskii functions (LKFs) is established, in which time delay and fractional-order information are fully taken into accoun...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/7/12/876 |
| _version_ | 1797380990899847168 |
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| author | Junzhou Dai Lianglin Xiong Haiyang Zhang Weiguo Rui |
| author_facet | Junzhou Dai Lianglin Xiong Haiyang Zhang Weiguo Rui |
| author_sort | Junzhou Dai |
| collection | DOAJ |
| description | This paper studies the asymptotic stability of fractional-order neural networks (FONNs) with time delay utilizing a sampled-data controller. Firstly, a novel class of Lyapunov–Krasovskii functions (LKFs) is established, in which time delay and fractional-order information are fully taken into account. Secondly, by combining with the fractional-order Leibniz–Newton formula, LKFs, and other analysis techniques, some less conservative stability criteria that depend on time delay and fractional-order information are given in terms of linear matrix inequalities (LMIs). In the meantime, the sampled-data controller gain is developed under a larger sampling interval. Last, the proposed criteria are shown to be valid and less conservative than the existing ones using three numerical examples. |
| first_indexed | 2024-03-08T20:45:54Z |
| format | Article |
| id | doaj.art-9c7a63a112b2410ca5a1f50e17c18f50 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-08T20:45:54Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-9c7a63a112b2410ca5a1f50e17c18f502023-12-22T14:10:03ZengMDPI AGFractal and Fractional2504-31102023-12-0171287610.3390/fractalfract7120876Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data ControlJunzhou Dai0Lianglin Xiong1Haiyang Zhang2Weiguo Rui3School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, ChinaSchool of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, ChinaSchool of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, ChinaSchool of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, ChinaThis paper studies the asymptotic stability of fractional-order neural networks (FONNs) with time delay utilizing a sampled-data controller. Firstly, a novel class of Lyapunov–Krasovskii functions (LKFs) is established, in which time delay and fractional-order information are fully taken into account. Secondly, by combining with the fractional-order Leibniz–Newton formula, LKFs, and other analysis techniques, some less conservative stability criteria that depend on time delay and fractional-order information are given in terms of linear matrix inequalities (LMIs). In the meantime, the sampled-data controller gain is developed under a larger sampling interval. Last, the proposed criteria are shown to be valid and less conservative than the existing ones using three numerical examples.https://www.mdpi.com/2504-3110/7/12/876fractional-order Leibniz–Newton formulafractional-order neural networksLyapunov–Krasovskii functionsasymptotic stability |
| spellingShingle | Junzhou Dai Lianglin Xiong Haiyang Zhang Weiguo Rui Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control Fractal and Fractional fractional-order Leibniz–Newton formula fractional-order neural networks Lyapunov–Krasovskii functions asymptotic stability |
| title | Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control |
| title_full | Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control |
| title_fullStr | Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control |
| title_full_unstemmed | Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control |
| title_short | Improved Results on Delay-Dependent and Order-Dependent Criteria of Fractional-Order Neural Networks with Time Delay Based on Sampled-Data Control |
| title_sort | improved results on delay dependent and order dependent criteria of fractional order neural networks with time delay based on sampled data control |
| topic | fractional-order Leibniz–Newton formula fractional-order neural networks Lyapunov–Krasovskii functions asymptotic stability |
| url | https://www.mdpi.com/2504-3110/7/12/876 |
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